Retail Banking Analytics_Marketelligent

1. Application of Decision Sciences
to Solve Business Problems
Retail Banking Industry

2. Analytics
for Retail
Banks

3. A Strong P&L
Discipline to all
Analytics
New Accounts Acquired
Accounts Closed
Account Activation rate
Payment Rate
Total Ending Receivables
Interest
Cost of Funds
Net Interest Margin
Risk-based Fees
Interchange
Affinity Rebates
Cross-Sell
Annual Fees
Net Credit Losses
Net Credit Margin
Operating Expenses
Loan Loss reserve
Net Income
REVENUESEXPENSES
Bank P&L
Acquire New Customers
- Segments X Products X Channel
- Mailbase Expansion
- Pricing
Reduce Customer Attrition
- Voluntary / Involuntary
- Retention Strategies
- Winback
Increasing Activation Rates
- Deepening Engagement
- Inactive Customer
Treatment
Improve Profitability of
Assets
- Balance Transfer
- Credit Line Strategies
- Pricing
Maximizing Interest Revenue
- Product Pricing
- Customer Behavior – Revolvers,
transactors, etc
Maximizing Fee Revenue
- Over Credit limit
- Delinquency
- Bad Check
Reduce Net Credit
Losses
- Credit Line
strategies
- Pricing strategies
- Collections
Increasing Cross-sell Revenues
- Revenue Enhancing Products
- Breadth of relationships
Marketelligent brings a top-down approach to all Analytics. Our analytics expertise impacts all line
items of a Business P&L

4. Strategic
Reporting
Strategic MIS & Reporting
Having a multi-dimensional view of critical business metrics available in real-time is key to effective
management of a Business. Marketelligent helps banks in putting together Strategic MIS across Product and
Functions without having to invest heavily in bespoke IT systems and BI packages. Our capabilities include
ETL, development of analytical databases, identification of key metrics and views, SAS-based data
manipulation and report delivery on visually-rich platforms.
Sample Reports include:
 Acquisitions and profile of New Customers
 Portfolio & Vintage performance
 Credit lines and Utilization
 Delinquency, Risk, Loss and Collections
 Product P&L’s and trends across time

5. Marketing
Analytics
Marketing Analytics
Marketing Analytics covers all functions of a Bank that help the business get a better understanding of its
Customers thereby leading to a deeper Customer Engagement and enhanced revenues. Marketelligent
brings unique expertise in key functions across the Customer Lifecycle:
 Customer Segmentation
 Customer Lifetime Value (CLV)
 Increasing Usage and building profitable balances
 Campaign design, management and tracking
 Measurement of marketing effectiveness
 Cross-sell and up-sell
 Customer Retention and win-back
 Marketing Response Scorecard development, validation and maintenance
Acquisition Usage & Loyalty
Value
Time
Retention
Activation

6. Marketing
Analytics
Customer Segmentation
In today’s competitive business scenario with customers having a multitude of options, their preferences and
buying patterns have been constantly evolving. For retaining the profitable and loyal customers, it is
therefore necessary to keep track of changing customer trends and accordingly tailor the offerings.
Segmentation is the practice of identifying homogenous groups of customers based on their needs, attitudes,
interests and purchase behavior. It enables identifying profitable customer segments and customizing
product and service offerings and marketing campaigns to target them effectively. It is typically done using a
combination of transaction data, demographic data and psychographic information.
It aids in answering critical business questions like:
 Which are the most profitable and loyal customer segments and how do we have tailored offerings for
these segments?
 How do we have special promotion campaigns, specifically to reach the high value customer segments?
 What are the revenue and profit contributions by different customer segments?
Platinum: Current investment > 50K
Gold: Current investment > 5K; < 50K
Silver: Current investment < 5K
Tenure<12mo
All Customers
1,889
1,637 MM EAD
87k AED/Customer
New Customers
4,568 (24%)
433 MM EAD (27%)
95k AED/Customer
Existing Customers
11,573 (76%)
1,203 MM EAD
(73%)
84k AED/Customer
Savers
2,944 (16%)
39 MM EAD (2%)
13k AED/Customer
Investors
7,316 (38%)
812 MM EAD (50%)
111k
AED/Customer
Redeemers
871 (5%)
60 MM EAD (4%)
69k AED/Customer
Revolvers
3,190 (17%)
292 MM EAD (18%)
92k AED/Customer
Platinum
Gold
Silver
Platinum
Gold
Silver
Platinum
Gold
Silver
Platinum
Gold
Silver
Platinum
Gold
Silver

7. Marketing
Analytics
Customer Lifetime Value
Customer Lifetime Value (CLV) represents how much a customer is worth in monetary terms and is based on
customer’s expected retention and spending rate. It can be defined as the present value of the total profit
expected from the customers during the entire period they do business with the company. CLV analysis uses
customers’ past transaction data and employs predictive modelling techniques to forecast how much each
customer would contribute to the company’s revenues and profits till they remain with the company and do
not attrite. The analysis can also be extended to estimate the lifetime values of new customers. CLV analysis
takes into account estimated annual profits from customers, duration of business relation of the customer,
and the discount rate to assess the net present value of the customers.
CLV analysis is used for:
 Forecasting the expected revenue from new customers and weighing it against the acquisition and
retention cost for them
 Deciding how much to spend on marketing programs for different customers
 Identifying the high value customer segments that can contribute the maximum to company’s revenue
and have special offers for them
 Identify the prospects who can become profitable for the company
Monthly
Expenses
Monthly
Net Revenues
Customer
Tenure
Net Margin
Accumulated
Margin
Acquisition
Costs
Customer Lifetime
Value
Predict monthly Revenues
Predict Customer Attrition
Predict Response RatesFrom existing P&L’s

8. Marketing
Analytics
Profitability & Loyalty analysis
For the sustainable growth of any enterprise, it is very important to identify the most profitable and loyal
customers. Having special schemes for these customers in form of offers and discounts, can help in realizing
the long term goals of increasing profits and expanding customer base.
Organizations use customer profitability and loyalty analysis to identify the most valuable customer segments
to prioritize marketing, sales and service investments. Transactional behaviour is analysed for creating a
Customer Value Score (C-score) for each customer, which explains their engagement levels. The C-Score can
be leveraged for proactive action to defend, retain and grow the customer base.
This can help answer key business questions like:
 Which are the most profitable and loyal customer segments and how much they contribute to the firm’s
profit?
 Which are the customer segments to be targeted for marketing programs and special offers?
 Which are the customer segments that can have a negative impact on the company’s profitability?
0%
10%
20%
30%
50%
ProfitContribution
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Watch Out:
Customers accounting
for losses.
Focus:
Customers who
have growth
potential
Sustain:
Highly profitable customers
Invest & Sustain:
Profitable customers
%Customers
Invest :
Highly profitable
customers
40%

9. Cross Selling & Upselling Strategies
It is not just enough to retain the profitable and loyal customers, but it has become a necessity to increase
the revenue contribution from the existing customer base. Cross Sell involves the sale of additional items in
order to increase the wallet share from the customers.
Market basket analysis is the technique used to evaluate customers’ purchasing behaviour and to identify the
different items bought together in the same shopping session. It uses transactional data and employs
predictive modelling techniques to identify customers’ preferences based on the associations between the
products recently purchased. It helps to determine which products are to be offered and which are the
customer segments most likely to be receptive to these cross selling propositions.
It aids in strengthening relations with customers by:
 Customizing layouts, product assortments and pricing so that it appeals to the customers
 Designing effective affinity promotions
• Stimulating trials and increase customer awareness during launch of new products and variants
• Handling excess stock by designing offers among associated products
Marketing
Analytics
CONFIDENCE Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product8
Product 1 100% 25% 9% 6% 18% 2% 28% 31%
Product 2 42% 100% 7% 8% 22% 6% 29% 22%
Product 3 31% 16% 100% 5% 10% 4% 18% 17%
Product 4 35% 29% 8% 100% 28% 7% 26% 12%
Product 5 47% 35% 8% 12% 100% 3% 37% 24%
Product 6 37% 66% 18% 19% 21% 100% 25% 21%
Product 7 45% 28% 8% 7% 23% 2% 100% 25%
Product 8 57% 24% 9% 3% 17% 2% 29% 100%
Probability that Product 8 is purchased given that Product 1 is bought is 31%
Probability that Product 1 is purchased given that Product 8
is bought is 57%
Increasing sales by creating cross-selling opportunities using MBA

10. Marketing
Analytics
Customer Retention
To retain customers, it is very essential to keep tracking customers’ activity regularly — their frequency of
shopping, evolution of their shopping patterns, how often do they shop and so on. Customers attrite on a
definite path to inactivity which can be identified and therefore managed. Also, acquiring new customers has
become far more expensive than retaining existing ones and hence customer retention has become a major
corporate priority. By employing attrition analysis, customers whose engagement levels have lowered and
who are likely to attrite can be identified and appropriate retention strategies can be formulated.
Churn analysis helps answer key business questions like:
 Which are the customer segments, to be targeted for retention programs?
 How do we identify the factors which are most likely to drive customers to remain:
• Creating segments based on preferences and buying patterns so that right offers can be made to the
right people
• Understand the variables that make high-value customers most likely to purchase and offer
incentives and personalized service
AttritionRate%
0%
10%
20%
30%
40%
50%
60%
70%
-
1
2
3
4
5
0 - 0.5 year 0.5 - 1 year 1 - 3 years 3 - 5 years > 5 years
#Customers(MM)

11. Risk & Fraud
Analytics
Risk & Fraud Analytics
Risk Analytics covers all areas of the business that directly impact loan losses and charge-off’s.
Marketelligent brings deep expertise in designing and implementing profit-based strategies that enable a
business to limit its losses; at the same time not compromising on revenue opportunities.
 Acquisition Credit Policy
 Pricing and Credit Line Management
 Collections and Recoveries
 Fraud detection and management
 Scorecard development, validation and maintenance across functions : approval, delinquency,
collections, etc
Behavioral Models Revenue and Cost
Drivers
Optimal Line Determination Optimal Line Drivers
Balance
Model
Revolve
Model
Risk
Model
CMV
LOCOptimalLOC
Revenue
LOC
Cost
LOC
Predicted V/s Actual Inactivity
0.00
50.00
100.00
150.00
200.00
250.00
300.00
0 50 100 150 200 250 300 350 400
Predicted Inactivity
Ideal
Actual
Predicted
Illustrative process for assigning
Optimal Line of Credit (LOC)
Other
Models

12. Business Situation :
The insurance provider generated leads through cross-selling. Potential customers were targeted in a 4-stage process, and they generally
displayed 4 possible outcomes:
Resources were being wasted on pursuing unlikely Prospects classified in red boxes above. The insurance provider wanted to determine which
members were more likely to complete at each stage , and then fast track the application through the approval process. By reducing the
proportion of declined and incomplete applications, operating costs could be optimized.
The Task :
- Develop a framework of predictive models to calculate the probability of a prospect purchasing the insurance product
- Get a more targeted base of Prospects, and hence reduce costs by removing prospects with least probability of buying the Product
Analytical Framework :
- Historical data , which contained information from both, internal and external sources, was analyzed
- Logistic models were built for identifying separate probabilities for each stage of the approval process
- Testing was done if Oversampling or Undersampling would improve the performance of the predictive models
- All the models were then combined to identify the ‘best’ leads
- The models were validated and implemented as SAS Macros to enable real-time scoring
The Result :
• The framework of 3 models provided useful insights on probabilities associated with approvals and important factors affecting it
• Up to 25% of total applications were removed with a loss of just 5% of Paid customers
• With costs going down by 25%, we were able to achieve an increment 14% net profits.
Client : An Insurance Provider in the US
Targeted
Prospects
Approved
Completed
Forms
Paid Premium
Didn’t PayDid notDeclined
Target : 100
Approved : 85
Completed: 68 Paid : 58
Predictive Models
Target : 75
Approved : 70
Completed: 62 Paid : 55
Analytics in Action
Targeted Prospecting. Increasing Profits by 14%.

13. Business Situation:
The client, a South-east Asia based Retail Bank encountered a significant increase in customer churn on their Card Portfolio despite having a
tried and tested loyalty program in place. This resulted in a 4.6 % drop in Balances during Q2 2012. The business wanted to monitor and
control Customer churn at regular intervals.
The Task:
To develop and implement a program that monitors Customer engagement levels and attrition risk, measure business impact from Customer
churn, and develop actionable strategies to manage Customer Attrition.
Analytical Framework:
High value customers that left the business impacted Balances significantly. Segments were developed to slot each high-value Customer on
the basis of recent purchase patterns. Movement of Customers across segments and over time was used to identify the level of ‘engagement’
the Customer had with the Business. Segment-specific offers and campaigns were implemented to manage customer attrition. Results from
the campaigns were used to continuously refine targeting and messaging.
The Result:
• Based on the analysis, the Business was able to identify high value Customers at risk of attrition. Suitable Retention programs were
designed and implemented for these Customers.
• Business was able to more efficiently utilize its Retention budget as targeted customers consisted of only 15% of the overall customer base
• Balances in Q4 2012 were up by an average of 2.1 % as compared to the previous two quarters.
Analytics in Action
Proactively Retaining your most Valuable Customers
Client: A Leading South-east Asian Bank
10%
39%21%
32%
39%
24%30%
6%
# of customers Revenue contribution
0%
25%
50%
75%
100%
High Medium-High Medium-Low Low

14. Business Situation :
The Client - a provider of short-term payday loans - was experiencing high default rates in its loan portfolio. Payday loans are instant small
value, short term loans. These loans are received by the borrower and are to be returned back on their next payday along with requisite fees
to the lender.
Default rates were highly seasonal and geography specific.
The Task :
- Develop and implement a ‘First Payday Default (FPD)’ Risk Model. The model will be used at the point of Acquisition to screen out Prospects
with a high probability of first payment default.
- Refine Acquisition Business strategy across key functions – pricing, loan amount and loan term.
Analytical Framework :
A 3-step analytical process was used:
1. Segmentation of Customer Base: Segmented Customers based on loan performance, demographics and credit bureau profile. Identified
and tagged High/Medium/Low FPD Customer Segments
2. Building a Default Scorecard: Identified Customers that have defaulted by Segment. Built predictive models for each segment. Model
identified Customers most at risk of defaulting in the first payment cycle
3. Building a holistic Strategy for execution: Created a strategy grid for executing business strategies. Recommend appropriate treatments by
Strategy grid
Segmentation Predictive Modeling Business Strategy
The Result :
The New FPD model was implemented in a robust test-control mode and results tracked:
• Geographic location of the Prospect was identified as a significant segmentation variable. 6 geographic segments were created and a FPD
model built for each segment.
• Seven credit bureau variables were identified as significant in identifying potential high-risk Prospects. Of these, ‘# inquiries in past 30 days’
and “# loans given in past 12 months’ were identified as most significant.
• Once implemented, the new predictive FPD models helped lower portfolio loan losses by 19%.
Analytics in Action
Lowering Portfolio Defaults by 19%
Client : A Consumer Finance Company making short-term Unsecured Loans
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Random New Model
%DefaultersCaptured
% Customers
Approval Model
Conversion Model
FPD Model
5+ cycle Risk Model
180 day Revenues
Reactivation Model
Acquisition
Risk
Revenue
• Individual
Scores
• Strategy Matrix
• Joint Scores
Old Model
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

15. Business Situation:
Being a service provider of online remittances, the client faced significant problems with fraud and money laundering. A lot of
time was being invested in manual review of all transactions, which in turn delayed the overall transaction processing time,
and also impacted customer satisfaction.
The Task:
To develop a process where only high risk transactions are sent for manual review, and rest are approved automatically.
The Analysis:
• Transaction history, compliance, service data along with external data sources like SSN responses and AMLOCK (Anti-
Money Laundering Database) data for high risk Customers was leveraged
• Critical variables defined that would serve as inputs to the Risk Score were divided into 5 broad categories - Geographic,
Historic, Identity, Transaction and Demographic - to make it easier to understand the reason for high risk during manual
review process
• Along with these variables some rules were also created based on compliance policy and Government regulations, which
when triggered automatically moved transaction to manual review irrespective of the risk score
• Multiple linear regression was performed to arrive at a transaction-level Risk score, and 4 Risk classes were
correspondingly defined (<25: Low, 25-50: Moderate, 50-75: High, 75-100: Extremely High)
The Implementation:
• A schedule of data extraction was setup to generate the Risk score before the KYC forms were prepared.
• The process was streamlined to ensure that all KYC forms carried a Risk score
• The Risk score and Risk class were populated on the KYC sheets
• Compliance committee continues to give it’s opinion on each KYC form (Low Risk to Extremely High Risk) and the
predictive model continues to be bootstrapped
The Result:
• The new Process reduced number of transactions reviewed manually from 700-800/day to 200/day, and also reduced the
average turnaround time for transactions from from 3.0 to 1.5 days.
• The process also helped the compliance team to identify which documents/clarifications to get from customer to process
the transaction.
Analytics in Action
Transaction-level Risk Assessment
Client : A Leading Service Provider of Online Remittances

16. Business Situation :
The Credit Card issuer was facing significant losses due to fraud in spite of having a real-time transaction scoring application. It flagged
suspicious transactions and declined them with a large false positive ratio, leading to bad customer experience and attrition. There were also
gaps in the process of identifying fraud with good accuracy due to constraints within the Fraud Operations group.
The Task :
- Develop optimized authorization rules that efficiently capture fraud with minimum impact on genuine transactions.
- Revisit the transaction scoring mechanism and suggest a methodology that is more customized for certain type of transactions. Retire non-
performing authorization rules.
- Develop new strategies/rules to better identify fraudulent transactions.
- Measure ‘agent performance’ in Fraud Operations Queues and suggest areas of improvement to Fraud Operations.
Analytical Framework :
A 5-step analytical process was used:
1. Historical card transaction logs, data on confirmed fraud cases for the past two years, Credit Bureau data and Card Association notifications
were leveraged for the analysis.
2. For Customer attrition analysis, accounts with a minimum 12 months-on-book at the time they were wrongly queued/declined were used,
their attrition rates, were then measured using a test-control approach.
3. Apart from an existing real-time fraud scoring engine, further analysis was conducted to identify domestic and international fraud hotspots.
4. Detailed segmentation was carried out on recent transactions to segregate fraud population with least impact on non-fraud population –
this led to new authorization rules.
5. Developed automated MIS reporting measuring existing rule performance, agent performance in Operation queues and a framework to
retire non-performing rules.
The Result :
• Fraud Detection rates improves by 70 basis points Year-on-year after implementing the new scoring layer incorporating localised scoring.
• Fraud Operations agents false positives and false negative ratios (identifying true fraud and non-fraud respectively) improved significantly
within 2 months of implementing Decision Quality framework. Missed dollar opportunity due to not identifying true fraud reduced by 15%
Year-on-year.
Analytics in Action
Combating Credit Card Transaction Fraud
Client : A Credit Card Issuer facing high Transaction Fraud

17. MANAGEMENT TEAM
GLOBAL EXPERIENCE.
PROVEN RESULTS.
Roy K. Cherian
CEO
Roy has over 20 years of rich experience in marketing, advertising and media
in organizations like Nestle India, United Breweries, FCB and Feedback
Ventures. He holds an MBA from IIM Ahmedabad.
Anunay Gupta, PhD
COO & Head of Analytics
Anunay has over 15 years of experience, with a significant portion focused
on Analytics in Consumer Finance. In his last assignment at Citigroup, he was
responsible for all Decision Management functions for the US Cards
portfolio of Citigroup, covering approx $150B in assets. Anunay holds an
MBA in Finance from NYU Stern School of Business.
Greg Ferdinand
EVP, Business Development
Greg has over 20 years of experience in global marketing, strategic planning,
business development and analytics at Dell, Capital One and AT&T. He has
successfully developed and embedded analytic-driven programs into a
variety of go-to-market, customer and operational functions. Greg holds an
MBA from NYU Stern School of Business
Kakul Paul
Business Head, CPG & Retail
Kakul has over 8 years of experience within the CPG industry. She was
previously part of the Analytics practice as WNS, leading analytic initiatives
for top Fortune 50 clients globally. She has extensive experience in what
drives Consumer purchase behavior, market mix modeling, pricing &
promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.
ADVANCED ANALYTICAL SOLUTIONS
MARKETELLIGENT, INC.
80 Broad Street, 5th Floor, New York, NY 10004
1.212.837.7827 (o) 1.208.439.5551 (fax) info@marketelligent.com
CONTACT www.marketelligent.com
Industry Business Focus Tools and Techniques
Consumer Finance Investment Optimization SAS, SPSS, R, VBA
Credit Cards Revenue Maximization Cluster analysis
Loans and Mortgages Cost and Process Efficiencies Factor analysis
Retail Banking & Insurance Forecasting Structural Equation Modeling
Wealth Management Predictive Modeling Conjoint analysis
Consumer Goods and Retail Risk Management Perceptual maps
CPG & Retail Pricing Optimization Neural Networks
Consumer Durables Customer Segmentation Chaid / CART
Manufacturing and Supply Chain Drivers Analysis Genetic Algorithms
High Tech OEM’s Supply Chain Management Support Vector Machines
Automotive Sentiment Analysis
Logistics & Distribution
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